Tkacik Group

Theoretical Biophysics and Neuroscience

How do networks built out of biological components – neurons, signaling molecules, genes, or even cooperating organisms – process information? In contrast to engineered systems, biological networks operate under strong constraints due to noise, limited energy, or specificity, yet nevertheless perform their functions reliably. The group uses biophysics and information theory to understand the principles and mechanisms behind this remarkable phenomenon.

How can cells in a multicellular organism reproducibly decide what tissue they are going to become? How do neurons in the retina cooperate to best encode visual information into neural spikes? How does the physics at the microscopic scale, which dictates how individual regulatory molecules interact with each other, constrain the kinds of regulatory networks that are observed in real organisms today, and how can such networks evolve? These are some of the questions addressed by the Tkačik group. About half of their time is dedicated to data-driven projects performed in close collaboration with experimentalists, and half on purely theoretical projects. Their goal is to develop theoretical ideas about biological network function and connect them to high-precision data.


On this site:


Team


Current Projects

Visual encoding in the retina | Genetic regulation during early embryogenesis | Collective dynamics | Evolution of gene regulation


Publications

Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 17(1), e1008529. View

Grah R, Zoller B, Tkačik G. 2020. Nonequilibrium models of optimal enhancer function. PNAS. 117(50), 31614–31622. View

Rizzo R, Zhang X, Wang JWJL, Lombardi F, Ivanov PC. 2020. Network physiology of cortico–muscular interactions. Frontiers in Physiology. 11, 558070. View

Kavcic B. 2020. Perturbations of protein synthesis: from antibiotics to genetics and physiology. IST Austria. View

Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 117(40), 25066–25073. View

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Career

since 2017 Professor, IST Austria
2011 – 2016 Assistant Professor, IST Austria
2008 – 2010 Postdoc, University of Pennsylvania, Philadelphia, USA
2007 Postdoc, Princeton University, USA
2007 PhD, Princeton University, USA


Selected Distinctions

2018 HFSP Grant
2012 HFSP Grant
2003 Burroughs-Wellcome Fellowship, Princeton University
2002 Golden Sign of the University of Ljubljana


Additional Information

Open Tkacik group website



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